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This self-funded PhD research project aims to advance the emerging research topics on physics-informed machine learning techniques with the targeted application on predictive maintenance (PdM
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control system that enhances Annual Energy Production (AEP), reduces mechanical stress, and improves fault detection using machine learning (ML) and physics-based modelling. The candidate will gain hands
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of the complex physics governing the interaction between the heat source and the material. Additionally, it seeks to develop an efficient modelling approach to accurately predict and control the temperature field
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and controlling defects and lay the foundation for a thermal physics-based approach to process qualification. Additive manufacturing (AM) is a rapidly evolving technology that continues to drive
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. While autonomy is becoming more integrated into modern mobility, the reliability of Position, Navigation and Timing (PNT) systems—especially in environments where GNSS signals are denied or degraded
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Cranfield University and Magdrive, offer a fully funded PhD position under the umbrella of the R2T2 consortium to study the optimisation of their thruster for a kick stage. R2T2 is a UKSA-funded
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. Understanding the process of droplet impact and freezing dynamics at high airspeeds, on textured and non-textured surfaces is critical to deciphering the physics behind ice adhesion and accretion. Previous work
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control system that enhances Annual Energy Production (AEP), reduces mechanical stress, and improves fault detection using machine learning (ML) and physics-based modelling. The candidate will gain hands
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testing) to understand and tailor the physical and chemical interactions within these complex structures. Cranfield University is internationally renowned for its research into materials for extreme
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support our staff and students to realise their full potential, from academic achievement to mental and physical wellbeing. We are committed to progressing the diversity and inclusion agenda, for example